In this task, you will be presented with a context from an academic paper and a question separated with a 
. You have to answer the question based on the context.

Example Input: Figure FIGREF23 shows the proposed ontology, which, in our evaluation procedure, was populated with 3121 events entries from 51 documents. 
 Question: How is the effectiveness of this pipeline approach evaluated?
Example Output: proposed ontology, which, in our evaluation procedure, was populated with 3121 events entries from 51 documents.

Example Input: We sparsify this graph to the union of a minimum spanning tree and a k-Nearest Neighbors (MST-kNN) graph BIBREF14, a geometric construction that removes less important similarities but preserves global connectivity for the graph and, hence, for the dataset. The MST-kNN graph is then analysed with Markov Stability BIBREF15, BIBREF16, BIBREF17, BIBREF18, a multi-resolution graph partitioning method that identifies relevant subgraphs (i.e., clusters of documents) at different levels of granularity. 
 Question: What cluster identification method is used in this paper?
Example Output: A combination of Minimum spanning trees, K-Nearest Neighbors and Markov Stability BIBREF15, BIBREF16, BIBREF17, BIBREF18

Example Input: We evaluate our pointer-generator performance using BLEU score. The baseline language model is trained using RNNLM BIBREF23 . Perplexity measure is used in the evaluation.

 
 Question: Did they use other evaluation metrics?
Example Output:
Yes

